{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:24:49Z","timestamp":1789658689786,"version":"4.0.1"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e0 degli Studi di Milano","id":[{"id":"https:\/\/ror.org\/00wjc7c48","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Purinergic Signalling"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Despite robust preclinical evidence, many clinical trials, including several that targeted the purinergic system, fail to demonstrate efficacy in humans. Failure may stem from inability to accurately identify patient subgroups responding similarly to treatments. Here, we explore the potential of artificial intelligence to revolutionize how we group and classify patients in clinical studies. We introduce a new framework using Large Language Models-generated embeddings of detailed patient data, to create a semantic-aware latent space, enabling us to identify truly meaningful patients\u2019 clusters. Large Language Models can provide explainable groupings, giving clear reasons why certain patients respond similarly to treatments. We present an example of successful application of this approach through the re-analysis of the AMARANTH clinical trial (NCT02245737, involving\u2009~\u20092200 patients and completed in 2018) testing\n                    <jats:italic>Lanabecestat<\/jats:italic>\n                    , a BACE1 inhibitor decreasing \u03b2-amyloid production in Alzheimer\u2019s disease, for which traditional analysis reported no efficacy. As in the original trial, our simulation showed no overall benefit. However, re-analysis\n                    <jats:italic>per<\/jats:italic>\n                    patients\u2019 clusters and subjects\u2019 re-stratification by semantic similarities (shared symptom profiles, progression patterns) identified a patients\u2019 subgroup in one of the clusters showing Lanabecestat-associated slower disease worsening, thus succeeding where the full trial had failed. By making a new therapy available to at least a subset of patients with a defined disease, this new approach may help maximize the return on drug development and reduce the burden on healthcare. Moreover, it will significantly improve the precision, efficiency and interpretability of clinical trials, paving the way for a new era of personalized medical treatments.\n                  <\/jats:p>","DOI":"10.1007\/s11302-026-10165-3","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:13:56Z","timestamp":1780064036000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data"],"prefix":"10.1007","volume":"22","author":[{"given":"Maria P.","family":"Abbracchio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"issue":"4","key":"10165_CR1","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s11302-022-09896-w","volume":"18","author":"S Han","year":"2022","unstructured":"Han S, Suzuki-Kerr H, Vlajkovic SM, Thorne PR (2022) The developmental journey of therapies targeting purine receptors: from basic science to clinical trials. Purinergic Signal 18(4):435\u2013450. https:\/\/doi.org\/10.1007\/s11302-022-09896-w","journal-title":"Purinergic Signal"},{"key":"10165_CR2","doi-asserted-by":"crossref","first-page":"10845","DOI":"10.1038\/s41467-024-55120-1","volume":"15","author":"J Schubert","year":"2024","unstructured":"Schubert J, Pereira JB, Janelidze S et al (2024) AI-guided patient stratification enhances clinical trial outcomes in Alzheimer\u2019s disease. Nat Commun 15:10845","journal-title":"Nat Commun"},{"key":"10165_CR3","unstructured":"Wang, F., Ceravolo, P., & Damiani, E. (2025). Comprehensive attribute encoding and dynamic LSTM hyper-models for outcome-oriented predictive business process monitoring. https:\/\/arxiv.org\/abs\/2506.03696"},{"key":"10165_CR4","unstructured":"Huang, K., Altosaar, J., & Ranganath, R. (2019). ClinicalBERT: modeling clinical notes and predicting hospital readmission. https:\/\/arxiv.org\/abs\/1904.05342"},{"issue":"4","key":"10165_CR5","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234\u20131240. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","journal-title":"Bioinformatics"},{"key":"10165_CR6","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., & Melville, J. (2018). UMAP: uniform manifold approximation and projection for dimension reduction. https:\/\/arxiv.org\/abs\/1802.03426.","DOI":"10.21105\/joss.00861"},{"key":"10165_CR7","doi-asserted-by":"publisher","unstructured":"Su, X., Grabowska, M.E., Kullo, I.J., Luo, Y., Smoller, J.W., Wei, W.-Q., Jarvik, G., Mooney, S., & Crosslin, D. (2024). Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis. Research Square [Preprint]. https:\/\/doi.org\/10.21203\/rs.3.rs-4708839\/v1.","DOI":"10.21203\/rs.3.rs-4708839\/v1"},{"key":"10165_CR8","doi-asserted-by":"publisher","DOI":"10.2196\/65537","volume":"27","author":"H Yang","year":"2025","unstructured":"Yang H, Li J, Zhang C, Pazos Sierra A, Shen B (2025) Large language model\u2013driven knowledge graph construction in sepsis care using multicenter clinical databases: development and usability study. J Med Internet Res 27:e65537","journal-title":"J Med Internet Res"},{"key":"10165_CR9","doi-asserted-by":"crossref","unstructured":"Ruta, D., Damiani, E., & Gabry\u015b, B. (2025, July). Scalable hyperbox clustering for geospatial data. In 2025 IEEE International Conference on Fuzzy Systems (FUZZ) (pp. 1\u20134). IEEE.","DOI":"10.1109\/FUZZ62266.2025.11152147"},{"key":"10165_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01806-9","volume":"8","author":"A Smith","year":"2025","unstructured":"Smith A, Beaney T, Hockham C, Su B, Elliott P, Downey L, Denaxas S, Barnaghi P, Dehghan A, Tzoulaki I (2025) Identifying clusters of people with multiple long-term conditions using large language models: a population-based study. npj Digit Med 8:Article 453","journal-title":"npj Digit Med"},{"issue":"1","key":"10165_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s44172-025-00453-y","volume":"4","author":"M Mesinovic","year":"2025","unstructured":"Mesinovic M, Watkinson P, Zhu T (2025) Explainability in the age of large language models for healthcare. Commun Eng 4(1):128. https:\/\/doi.org\/10.1038\/s44172-025-00453-y","journal-title":"Commun Eng"},{"issue":"2","key":"10165_CR12","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1001\/jamaneurol.2019.3988","volume":"77","author":"AM Wessels","year":"2019","unstructured":"Wessels AM, Tariot PN, Zimmer JA, Selzler KJ, Bragg SM, Andersen SW, Landry J, Krull JH, Downing AM, Willis BA, Shcherbinin S, Mullen J, Barker P, Schumi J, Shering C, Matthews BR, Stern RA, Vellas B, Cohen S, Sims JR (2019) Efficacy and safety of Lanabecestat for treatment of early and mild Alzheimer disease: the AMARANTH and DAYBREAK-ALZ randomized clinical trials. JAMA Neurol 77(2):199\u2013209. https:\/\/doi.org\/10.1001\/jamaneurol.2019.3988","journal-title":"JAMA Neurol"},{"issue":"1","key":"10165_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-61355-3","volume":"16","author":"D Vaghari","year":"2025","unstructured":"Vaghari D, Mohankumar G, Tan K, Lowe A, Shering C, Tino P, Kourtzi Z (2025) AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer\u2019s disease clinical trial. Nat Commun 16(1):6244","journal-title":"Nat Commun"}],"updated-by":[{"DOI":"10.1007\/s11302-026-10189-9","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000}}],"container-title":["Purinergic Signalling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11302-026-10165-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11302-026-10165-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11302-026-10165-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T07:02:28Z","timestamp":1789628548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11302-026-10165-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":13,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10165"],"URL":"https:\/\/doi.org\/10.1007\/s11302-026-10165-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-8808564\/v1","asserted-by":"object"}]},"ISSN":["1573-9538","1573-9546"],"issn-type":[{"value":"1573-9538","type":"print"},{"value":"1573-9546","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]},"assertion":[{"value":"6 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11302-026-10189-9","URL":"https:\/\/doi.org\/10.1007\/s11302-026-10189-9","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Clinical trial number","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"54"}}